from datetime import datetime

import matplotlib.pyplot as plt
import torch


def 				lowerCAmelCase__       (			a__ )      ->Tuple:




  '''simple docstring'''

  for param in module.parameters():
    _UpperCamelCase           =    False


def 				lowerCAmelCase__       (			)      ->Tuple:




  '''simple docstring'''

  _UpperCamelCase           =    "cuda" if torch.cuda.is_available() else "cpu"
  if torch.backends.mps.is_available() and torch.backends.mps.is_built():
    _UpperCamelCase           =    "mps"
  if device == "mps":
    print(
        "WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
        " errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
        " with generations." )
  return device


def 				lowerCAmelCase__       (			a__ )      ->Tuple:




  '''simple docstring'''

  _UpperCamelCase           =    plt.imshow(a__ )
  fig.axes.get_xaxis().set_visible(a__ )
  fig.axes.get_yaxis().set_visible(a__ )
  plt.show()







def 				lowerCAmelCase__       (			)      ->Optional[Any]:




  '''simple docstring'''

  _UpperCamelCase           =    datetime.now()
  _UpperCamelCase           =    current_time.strftime("%H:%M:%S" )
  return timestamp





def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__    ,     a__ )      ->int:




  '''simple docstring'''

  if index == number_of_items:
    return 0
  _UpperCamelCase           =    0
  _UpperCamelCase           =    0
  _UpperCamelCase           =    knapsack(a__    ,     a__    ,     a__    ,     a__    ,     index + 1 )
  if weights[index] <= max_weight:
    _UpperCamelCase           =    values[index] + knapsack(
        a__    ,     a__    ,     a__    ,     max_weight - weights[index]    ,     index + 1 )
  return max(a__    ,     a__ )


if __name__ == "__main__":
   import doctest

   doctest.testmod()




